---
title: "searchlogit (Python package): papers citing it"
description: "3 papers in the tracked journals link to searchlogit (Python package) in their Crossref records, the first in 2024; most of them (2) in 2024."
url: https://scholar4ai.com/code/pypi-searchlogit
markdown_url: https://scholar4ai.com/code/pypi-searchlogit.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "3 papers in the tracked journals link to searchlogit (Python package) in their Crossref records, the first in 2024; most of them (2) in 2024. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# searchlogit (Python package): papers linking to it

Research papers in the 480 tracked journals that link to searchlogit (Python package) (https://pypi.org/project/searchlogit/), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- PyPI
- https://pypi.org/project/searchlogit/

All repositories: https://scholar4ai.com/code.md

## Key findings

- 3 papers in the tracked journals link to searchlogit (Python package) in their Crossref records, the first in 2024; most of them (2) in 2024.
- By category: Operations Research (3); the journals with the most are Transportation (2) and Transportation Research Part C Emerging Technologies (1).
- 3 of the papers build on one another: a later paper cites an earlier one that also links searchlogit (Python package).

## By year

*Papers linking to searchlogit (Python package) by publication year*

| Year | Papers |
|---|---:|
| 2024 | 2 |
| 2025 | 1 |

## Journals

*Journals whose papers link to searchlogit (Python package) most*

| Journal | Papers |
|---|---:|
| [Transportation](https://scholar4ai.com/journals/transportation.md) | 2 |
| [Transportation Research Part C Emerging Technologies](https://scholar4ai.com/journals/transportation-research-part-c-emerging-technologies.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Estimation of discrete choice models considering simultaneously multiple objectives and complex data characteristics](https://doi.org/10.1016/j.trc.2024.104517) (Transportation Research Part C Emerging Technologies, 2024)
  - [Retiring meters: a roadmap to encourage wider adoption of parking apps](https://doi.org/10.1007/s11116-024-10553-x) (Transportation, 2024) — cites the paper above
  - [Enhanced utility estimation algorithm for discrete choice models in travel demand forecasting](https://doi.org/10.1007/s11116-024-10579-1) (Transportation, 2025) — cites the paper above

## Papers

*Papers linking to searchlogit (Python package), most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2024-02-13 | [Estimation of discrete choice models considering simultaneously multiple objectives and complex data characteristics](https://doi.org/10.1016/j.trc.2024.104517) | [Transportation Research Part C Emerging Technologies](https://scholar4ai.com/journals/transportation-research-part-c-emerging-technologies.md) | Prithvi Bhat Beeramoole, Ryan Kelly, Md. Mazharul Haque et al. | 21 | cites it |
| 2025-02-05 | [Enhanced utility estimation algorithm for discrete choice models in travel demand forecasting](https://doi.org/10.1007/s11116-024-10579-1) | [Transportation](https://scholar4ai.com/journals/transportation.md) | Amir Ghorbani, Neema Nassir, Patrícia Sauri Lavieri et al. | 13 | cites it |
| 2024-12-05 | [Retiring meters: a roadmap to encourage wider adoption of parking apps](https://doi.org/10.1007/s11116-024-10553-x) | [Transportation](https://scholar4ai.com/journals/transportation.md) | Mohammad Mahdi Ahmadian, Prithvi Bhat Beeramoole, Douglas Charles Baker et al. | 6 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). searchlogit (Python package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/pypi-searchlogit

```bibtex
@misc{scholar4ai-code-pypi-searchlogit,
  author = {{Scholar4AI}},
  title = {searchlogit (Python package): papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/pypi-searchlogit}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).